Record Year for Investment in Medical Imaging AI Companies
Published: November 16, 2017
With around six weeks of the year to go, 2017 is already a record year for capital funding of companies who are developing machine learning solutions for medical imaging. There have been 15 deals worth a combined $152 million, up by more than 90% on 2016.
You can download our updated analysis report for free here.
The five biggest deals of the year are shown in the table below.
Some of the key takeaways from our analysis are:
- There are now over 70 start-ups developing machine learning solutions for medical imaging. 36 of these have entered the market since the start of 2015.
- The size of funding rounds has notably increased in 2017. There have been 11 deals worth $5M or more.
- The most funded companies are Arterys ($43.5M), VoxelCloud ($30.5M), DeepWise ($28M) and Infervision ($27M).
- Over half (51.7%) of the funding awarded in 2017 has gone to companies based in Asia, mostly to Chinese companies.
- Four new companies entered the market in 2017, down from 15 new entrants in 2016. Only one of the four has received external funding.
One of the most striking trends in 2017 has been the surge of investment in Chinese developers of medical imaging AI solutions. The second most funded company, VoxelCloud, is a US-Chinese company and the third (Deepwise) and fourth (Infervision) most funded companies are both Chinese. A fourth Chinese company, Huiyi Hui Ying, also features in the list of the ten most funded companies. As discussed in our recent insight, “China – The Market Maker for AI in Medical Imaging?”, this is not overly surprising given the chronic shortage of skilled medical professionals in China and the wider availability of large imaging datasets to the train the algorithms. With Chinese tech giants such as Alibaba and Tencent also making moves in medical imaging AI, China looks set to become a leading global player, both in terms of technology innovation and consumption.Share on LinkedIn
Machine Learning in Medical ImagingTo be published: July 2018
Machine Learning in Medical Imaging
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